Designation -AI Infrastructure System Performance Architect

Experience Level: 8–15+ Years

Role Type: Architect

Location: Bangalore / Hyderabad (Hybrid)

Position SummaryAnalyze system-level performance, scalability, and bottlenecks of the company’s chiplet-based AI infrastructure using virtual platforms and performance models.

Key Responsibilities• Memory / Data-Movement Modelling: Analyze memory bandwidth and latency requirements, model XPU-to-memory and chiplet-to-memory traffic, and identify data-movement bottlenecks.

- Multi-Chiplet / Multi-XPU Scalability: Analyze scaling from single-XPU to multi-XPU systems, evaluate scale-up/scale-out architectures, and study bandwidth, latency, congestion, and resource utilization.
- AI Infrastructure System Modelling: Develop system-level performance models covering compute, interconnect, memory, and I/O interactions, and develop representative AI/HPC traffic patterns.
- System Performance & Architecture Exploration: Perform architecture trade-offs and analyze latency, bandwidth, throughput, utilization, queue depth, congestion, and bottlenecks; generate performance reports and architecture recommendations.

Required Technical Skills• 8–15+ years of system/performance architecture experience.

- AI/HPC infrastructure and performance modelling.
- SystemC/TLM.
- C++ / Python.
- PCIe / CXL / UCIe.
- HBM/DDR.




- System-level architecture.

Preferred Skills• XPU/GPU/NPU architecture.

- AI workload characterization.
- UALink and networking.
- Cluster/rack-scale architecture.
- Power-performance analysis.

Education• B.E. / B.Tech / M.E. / M.Tech in Electronics, Electrical, Computer Engineering, or related discipline.

Project Experience• Demonstrated system-level performance analysis (latency, bandwidth, throughput, utilization, congestion) on a chiplet-based or multi-XPU AI system.

Soft Skills• Strong analytical rigor in translating raw performance data into architecture recommendations.

- Ability to collaborate across connectivity, memory, and power architecture teams to build a coherent system view.

Positive to Have• Experience characterizing real AI/HPC workloads for use in performance models.

- Exposure to cluster or rack-scale system architecture.

Expected Deliverables• System-level performance models covering compute, interconnect, memory, and I/O.

- Architecture trade-off studies and performance reports with recommendations.
- Representative AI/HPC traffic pattern definitions for use across the team’s models.

Interested candidates may share resumes at [email protected](Immediate to 30-day notice period candidates preferred).

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